| 基于MapReduce的量子蚁群算法 |
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| 引用本文： | 贾瑞玉,李亚龙. 基于MapReduce的量子蚁群算法[J]. 计算机工程与应用, 2013, 49(19): 246-249 |
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| 作者姓名： | 贾瑞玉 李亚龙 |
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| 作者单位： | 安徽大学 计算机科学与技术学院，合肥 230601 |
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| 基金项目： | 安徽省教育厅自然科学研究基金资助重点项目(No.2011A006)。 |
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| 摘 要： | 量子蚁群算法是在蚁群算法的基础上结合量子计算而提出的，该算法具有较好的全局寻优能力和种群多样性。应用MapReduce的key/value编程模型，将量子蚁群算法并行化，提出了基于MapReduce的量子蚁群算法（MQACA），并将其部署到Hadoop云计算平台上运行。对0-1背包问题的测试结果证明，随着数据规模的扩大和并行程度的提高，MQACA具有良好的加速比和并行效率。
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| 关 键 词： | 量子蚁群算法 云计算 MapReduce模型 |
Quantum-inspired ant colony algorithm based on MapReduce model |
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| JIA Ruiyu , LI Yalong. Quantum-inspired ant colony algorithm based on MapReduce model[J]. Computer Engineering and Applications, 2013, 49(19): 246-249 |
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| Authors: | JIA Ruiyu LI Yalong |
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| Affiliation: | School of Computer Science and Technology, Anhui University, Hefei 230601, China |
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| Abstract: | The Quantum-inspired ant colony algorithm is a new algorithm which is based on the combination of ant colony optimization and quantum computing, and has better diversity and global search capacity. This paper aims at the parallelism of Quantum-inspired ant colony algorithm, uses cloud computing to parallel Quantum-inspired ant colony algorithm, makes it to meet the key/value programming model of MapReduce, puts forward MapReduce-based Quantum-inspired ant colony algorithm and runs the algorithm on Hadoop platform. Using 0-1 knapsack problem for test, with the expansion of data set, improvement of parallelism, MQACA exhibits good speed-up ratio and parallel efficiency, proves the feasibility of MQACA. |
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| Keywords: | Quantum-inspired ant colony algorithm cloud computing MapReduce model |
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